Blood lipid levels are highly heritable and modifiable risk factors for coronary artery disease (CAD), and are the leading cause of death worldwide. These facts have motivated human genetic association studies that have the substantial potential to define the risk factors that are causal and to identify pathways and therapeutic targets for lipids and CAD.
The success of the HapMap project that provided an extensive catalog of human genetic variations and the development of microarray based genotyping chips (typically containing variations with allele frequencies ＞5%) facilitated common variant association study (CVAS; formerly termed genome-wide association study, GWAS) identifying disease-associated variants in a genome-wide manner. To date, 157 loci associated with blood lipids and 46 loci with CAD have been successfully identified, accounting for approximately 12%–14% of heritability for lipids and 10% of heritability for CAD. However, there is yet a major challenge termed “missing heritability problem,” namely the observation that loci detected by CVAS explain only a small fraction of the inferred genetic variations. To explain such missing portions, focuses in genetic association studies have shifted from common to rare variants. However, it is challenging to apply rare variant association study (RVAS) in an unbiased manner because such variants typically lack the sufficient number to be identified statistically.
In this review, we provide a current understanding of the genetic architecture mostly derived from CVAS, and several updates on the progress and limitations of RVAS for lipids and CAD.